Vehicle Fuel Consumption Prediction Method Based on Driving Behavior Data Collected from Smartphones

Chang Liu,Yuelong Su,Zhenning Dong,Ying Yao,Xiaohua Zhao,Jian Rong,Yunlong Zhang

doi:10.1155/2020/9263605

Abstract

Transportation is an important factor that affects energy consumption, and driving behavior is one of the main factors affecting vehicle fuel consumption. The purpose of this paper is to improve fuel consumption monitoring databases based on mobile phone data. Based on the mobile phone terminals and on-board diagnostic system (OBD) installed in taxis, driving behavior data and fuel consumption data are extracted, respectively. By matching the driving behavior data collected by a mobile phone with the fuel consumption data collected by OBD, the correlation between driving behavior and fuel consumption is explored, so that vehicle fuel consumption could be predicted based on mobile phone data. The fuel consumption prediction models are built using back propagation (BP) neural network, support vector regression (SVR), and random forests. The results show that the average speed, average speed except for idle (ASEI), average acceleration, average deceleration, acceleration time percentage, deceleration time percentage, and cruising time percentage are important indicators for fuel consumption evaluation. All three models could predict fuel consumption accurately, with an absolute relative error less than 10%. The random forest model is proved to have the highest accuracy and runs faster, making it suitable for wide application. This method lays a foundation for monitoring database improvement and fine management of urban transportation fuel consumption.

Highlights

Vehicle energy consumption and pollutant emissions are key problems for the healthy and sustainable development of urban transportation
75% were randomly selected as training samples and the remaining data were test samples. e fuel consumption prediction models were constructed based on the back propagation (BP) neural network, support vector regression (SVR), and random forest
In this study, driving behavior data and fuel consumption data of taxi drivers collected from on-board diagnostic system (OBD) and mobile phone terminals, respectively, were matched. e correlation between driving behavior and fuel consumption was analyzed, and relevant driving behavior indicators affecting fuel consumption were extracted through the filter-based feature selection method

Summary

Introduction

Vehicle energy consumption and pollutant emissions are key problems for the healthy and sustainable development of urban transportation. With the continuous growth of car ownership in China, the energy consumption of its private cars increased 4.2 times, from 13.12 to 68.34 million tons of standard coal, from 2005 to 2015. E energy consumption of private cars will continue to increase before 2020, when it is expected to reach 117.38 million tons of standard coal [1]. Among many factors that affect the energy consumption of vehicles, driving behavior plays an important role. Providing drivers with continuous eco-driving feedback in the long term could lead to a 10% reduction in fuel consumption. Hiraoka et al [3] studied the influence of ecological driving behavior on fuel consumption and found that giving feedback on fuel consumption information to drivers could improve fuel economy by 10%. Ahn and Rakha [4] analyzed the influence of drivers’ route choice on vehicle fuel consumption, and the results indicated that energy consumption and exhaust emissions

Methods

Results

Conclusion